dropout.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.optimizers import RMSprop\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 20\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.reshape(60000, 784)\n",
    "x_test = x_test.reshape(10000, 784)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 1024)              803840    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 1,863,690\n",
      "Trainable params: 1,863,690\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 92s 2ms/step - loss: 0.4459 - acc: 0.8597 - val_loss: 0.1507 - val_acc: 0.9548\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 93s 2ms/step - loss: 0.2544 - acc: 0.9214 - val_loss: 0.1196 - val_acc: 0.9632\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 89s 1ms/step - loss: 0.2124 - acc: 0.9361 - val_loss: 0.1103 - val_acc: 0.9667\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 86s 1ms/step - loss: 0.1954 - acc: 0.9399 - val_loss: 0.1000 - val_acc: 0.9695\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 93s 2ms/step - loss: 0.1792 - acc: 0.9471 - val_loss: 0.0889 - val_acc: 0.9718\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 89s 1ms/step - loss: 0.1693 - acc: 0.9494 - val_loss: 0.0983 - val_acc: 0.9712\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 88s 1ms/step - loss: 0.1664 - acc: 0.9510 - val_loss: 0.0787 - val_acc: 0.9765\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 85s 1ms/step - loss: 0.1539 - acc: 0.9536 - val_loss: 0.0781 - val_acc: 0.9758\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 85s 1ms/step - loss: 0.1492 - acc: 0.9542 - val_loss: 0.0755 - val_acc: 0.9769\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 85s 1ms/step - loss: 0.1459 - acc: 0.9562 - val_loss: 0.0766 - val_acc: 0.9780\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 85s 1ms/step - loss: 0.1440 - acc: 0.9564 - val_loss: 0.0740 - val_acc: 0.9778\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 86s 1ms/step - loss: 0.1416 - acc: 0.9579 - val_loss: 0.0757 - val_acc: 0.9781\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 86s 1ms/step - loss: 0.1376 - acc: 0.9594 - val_loss: 0.0711 - val_acc: 0.9792\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 86s 1ms/step - loss: 0.1350 - acc: 0.9591 - val_loss: 0.0769 - val_acc: 0.9773\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 86s 1ms/step - loss: 0.1326 - acc: 0.9601 - val_loss: 0.0742 - val_acc: 0.9793\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 85s 1ms/step - loss: 0.1296 - acc: 0.9619 - val_loss: 0.0770 - val_acc: 0.9789\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 86s 1ms/step - loss: 0.1289 - acc: 0.9619 - val_loss: 0.0738 - val_acc: 0.9792\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 88s 1ms/step - loss: 0.1250 - acc: 0.9638 - val_loss: 0.0732 - val_acc: 0.9778\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 107s 2ms/step - loss: 0.1266 - acc: 0.9630 - val_loss: 0.0705 - val_acc: 0.9794\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 96s 2ms/step - loss: 0.1233 - acc: 0.9637 - val_loss: 0.0716 - val_acc: 0.9808\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(1024, activation='relu', input_shape=(784,)))\n",
    "model.add(Dropout(0.8))\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(x_test, y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_acc', 'val_loss', 'loss', 'acc'])\n",
      "[0.8597, 0.92135, 0.93605, 0.9399333333333333, 0.9471, 0.9493833333015442, 0.9509833333333333, 0.9536166666984558, 0.9542166666348775, 0.9561500000317892, 0.9564499999682109, 0.9578833333015442, 0.9593999999682109, 0.9591499999682108, 0.9600666666984559, 0.9618666666984558, 0.9619000000317891, 0.9637999999682109, 0.9629666666666666, 0.9636666666666667]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "print(history.history.keys())\n",
    "print(history.history['acc'])\n",
    "\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model acc [{}]'.format(history.history['acc'][-1]))\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "#plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_10 (Dense)             (None, 1024)              803840    \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 1,863,690\n",
      "Trainable params: 1,863,690\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 54s 898us/step - loss: 0.1928 - acc: 0.9417 - val_loss: 0.0957 - val_acc: 0.9672\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 40s 669us/step - loss: 0.0733 - acc: 0.9769 - val_loss: 0.0921 - val_acc: 0.9714\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 45s 758us/step - loss: 0.0475 - acc: 0.9848 - val_loss: 0.1039 - val_acc: 0.9690\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 41s 687us/step - loss: 0.0330 - acc: 0.9896 - val_loss: 0.0655 - val_acc: 0.9811\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 40s 659us/step - loss: 0.0286 - acc: 0.9904 - val_loss: 0.0762 - val_acc: 0.9766\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 39s 655us/step - loss: 0.0239 - acc: 0.9923 - val_loss: 0.0680 - val_acc: 0.9803\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 43s 723us/step - loss: 0.0209 - acc: 0.9930 - val_loss: 0.0780 - val_acc: 0.9812\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 60s 1ms/step - loss: 0.0162 - acc: 0.9949 - val_loss: 0.0930 - val_acc: 0.9777\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 77s 1ms/step - loss: 0.0193 - acc: 0.9934 - val_loss: 0.1027 - val_acc: 0.9758\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0146 - acc: 0.9951 - val_loss: 0.0661 - val_acc: 0.9849\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 40s 661us/step - loss: 0.0127 - acc: 0.9962 - val_loss: 0.0885 - val_acc: 0.9810\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 40s 674us/step - loss: 0.0150 - acc: 0.9957 - val_loss: 0.0816 - val_acc: 0.9806\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 40s 672us/step - loss: 0.0142 - acc: 0.9957 - val_loss: 0.0862 - val_acc: 0.9793\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 40s 666us/step - loss: 0.0133 - acc: 0.9961 - val_loss: 0.0744 - val_acc: 0.9830\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 40s 664us/step - loss: 0.0063 - acc: 0.9981 - val_loss: 0.0907 - val_acc: 0.9814\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 53s 891us/step - loss: 0.0147 - acc: 0.9957 - val_loss: 0.1167 - val_acc: 0.9776\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0147 - acc: 0.9953 - val_loss: 0.0886 - val_acc: 0.9831\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 42s 697us/step - loss: 0.0073 - acc: 0.9981 - val_loss: 0.0926 - val_acc: 0.9830\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 48s 808us/step - loss: 0.0042 - acc: 0.9989 - val_loss: 0.1225 - val_acc: 0.9770\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0089 - acc: 0.9974 - val_loss: 0.1142 - val_acc: 0.9804\n"
     ]
    }
   ],
   "source": [
    "model2 = Sequential()\n",
    "model2.add(Dense(1024, activation='relu', input_shape=(784,)))\n",
    "model2.add(Dense(1024, activation='relu'))\n",
    "model2.add(Dense(num_classes, activation='softmax'))\n",
    "model2.summary()\n",
    "\n",
    "model2.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history2 = model2.fit(x_train, y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                    verbose=1,\n",
    "                    validation_data=(x_test, y_test))\n",
    "score = model2.evaluate(x_test, y_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history2.history['acc'])\n",
    "plt.plot(history2.history['val_acc'])\n",
    "plt.title('model acc [{}]'.format(history2.history['acc'][-1]))\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "#plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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